Internet DRAFT - draft-irtf-nmrg-network-digital-twin-arch
draft-irtf-nmrg-network-digital-twin-arch
Network Management C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: 5 September 2024 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
Q. Wu
Huawei
M. Boucadair
C. Jacquenet
Orange
4 March 2024
Network Digital Twin: Concepts and Reference Architecture
draft-irtf-nmrg-network-digital-twin-arch-05
Abstract
Digital Twin technology has been seen as a rapid adoption technology
in Industry 4.0. The application of Digital Twin technology in the
networking field is meant to develop various rich network
applications and realize efficient and cost effective data driven
network management, and accelerate network innovation.
This document presents an overview of the concepts of Digital Twin
Network, provides the basic definitions and a reference architecture,
lists a set of application scenarios, and discusses the benefits and
key challenges of such technology.
Discussion Venues
This note is to be removed before publishing as an RFC.
Discussion of this document takes place on the Network Management
Research Group mailing list (nmrg@irtf.org), which is archived at
https://mailarchive.ietf.org/arch/browse/nmrg.
Source for this draft and an issue tracker can be found at
https://github.com/cheneyzhoucheng/network-digital-twin.
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Table of Contents
1. Introduction
2. Terminology
2.1. Acronyms & Abbreviations
2.2. Definitions
3. Introduction of Concepts
3.1. Background of Digital Twin
3.2. Digital Twin for Networks
4. Characteristics of Network Digital Twin
5. Benefits of Network Digital Twin
5.1. Optimized Network Total Cost of Operation
5.2. Optimized Decision Making
5.3. Safer Assessment of Innovative Network Capabilities
5.4. Privacy and Regulatory Compliance
5.5. Customized Network Operation Training
6. Challenges to Build Network Digital Twin
7. A Reference Architecture of Network Digital Twin
8. Enabling Technologies to Build Network Digital Twin
8.1. Data Collection and Data Services
8.2. Network Modeling
8.3. Network Visualization
8.4. Interfaces
8.5. Twinning Management
9. Interaction with Intent-Based Networking (IBN)
10. Sample Application Scenarios
10.1. Human Training
10.2. Machine Learning Training
10.3. DevOps-Oriented Certification
10.4. Network Fuzzing
10.5. Network Inventory Management
11. Research Perspectives: A Summary
12. Security Considerations
13. IANA Considerations
14. Open issues
15. Informative References
Acknowledgments
Authors' Addresses
1. Introduction
The fast growth of network scale and the increased demand placed on
these networks require them to accommodate and adapt dynamically to
customer needs, implying a significant challenge to network
operators. Indeed, network operation and maintenance are becoming
more complex due to higher complexity of the managed networks and the
sophisticated services they are delivering. As such, providing
innovations on network technologies, management and operation will be
more and more challenging due to the high risk of interfering with
existing services and the higher trial costs if no reliable emulation
platforms are available.
A Digital Twin is the real-time representation of a physical entity
in the digital world. It has the characteristics of virtual-reality
interrelation and real-time interaction, iterative operation and
process optimization, full life-cycle and comprehensive data-driven
network infrastructure. Currently, digital twin has been widely
acknowledged in academic publications and adopted in Industry 4.0.
See more in Section 3.
A digital twin for networks can be built by applying Digital Twin
technologies to networks and creating a virtual image of real network
facilities (called herein, emulation). Basically, the digital twin
for networks is an expansion platform of network emulation and can be
seen as a tool for scenario planning, impact analysis, and change
management. The main difference compared to conventional network
simulation is the interactive virtual-real mapping and data driven
approach to build closed-loop network automation. By integrating
network digital twin into the network management, it allows network
maintenance engineers to assess, model, and tweak optimization
strategies in a risk-free environment, ensuring that only the most
effective changes might be implemented in the real network (i.e.,
subject to adequate validation and control checks). Digital twin for
networks also play a crucial role in root cause analysis, providing a
sandbox for assessing hypotheses and validating the outcomes of data-
driven insights without impacting end users, when adequate isolation
guards are in place. Therefore, digital twin for networks is more
than an simulation platform or network simulator.
Through the real-time data interaction between the real network and
its twin network(s), the network digital twin platform might help the
network designers to achieve more simplification, automatic,
resilient, and full life-cycle operation and maintenance. More
specifically, the network digital twin can, thus, be used to develop
various rich network applications and assess specific behaviors
(including network transformation) before actual implementation in
the real network, tweak the network for better optimized behavior,
run 'what-if' scenarios that cannot be tested and evaluated easily in
the real network. In addition, service impact analysis tasks can
also be facilitated.
2. Terminology
2.1. Acronyms & Abbreviations
IBN: Intent-Based Networking
AI Artificial Intelligence
CI/CD: Continuous Integration/Continuous Delivery
ML: Machine Learning
OAM: Operations, Administration, and Maintenance
PLM: Product Lifecycle Management
2.2. Definitions
This document makes use of the following terms:
Digital Twin: Digital counterpart of a physical system (twin) that
captures its attributes, behavior, and interactions and is
(continually) updated with the latter's performance, maintenance,
and health status data throughout the physical system's life
cycle.
Network digital twin: A digital representation that is used in the
context of Networking and whose physical counterpart is a data
network or enterprise network. This is also called, digital twin
for networks. See more in Section 4.
Physical network: Object, system, process, software, or environment
that the digital twin is designed to replicate and represent
virtually.
3. Introduction of Concepts
3.1. Background of Digital Twin
The concept of the "twin" dates to the National Aeronautics and Space
Administration (NASA) Apollo program in the 1970s, where a replica of
space vehicles on Earth was built to mirror the condition of the
equipment during the mission [Rosen2015].
In 2003, Digital Twin was attributed to John Vickers by Michael
Grieves in his product lifecycle management (PLM) course as "virtual
digital representation equivalent to physical products"
[Grieves2014]. Digital twin can be defined as a virtual instance of
a physical system (twin) that is continually updated with the
latter's performance, maintenance, and health status data throughout
the physical system's life cycle [Madni2019]. By providing a living
copy of physical system, digital twins bring numerous advantages,
such as accelerated business processes, enhanced productivity, and
faster innovation with reduced costs. So far, digital twin has been
successfully applied in the fields of intelligent manufacturing,
smart city, or complex system operation and maintenance to help with
not only object design and testing, but also management aspects
[Tao2019].
Compared with 'digital model' and 'digital shadow', the key
difference of 'digital twin' is the direction of data between the
physical and virtual systems [Fuller2020]. Typically, when using a
digital twin, the (twin) system is generated and then synchronized
using data flows in both directions between physical and digital
components, so that control data can be sent, and changes between the
physical and digital objectives of systems are automatically
represented. This behavior is unlike a 'digital model' or 'digital
shadow', which are usually synchronized manually, lacking of control
data, and might not have a full cycle of data integrated.
At present (2024), there is no unified definition of digital twin
framework. The industry, scientific research institutions, and
standards developing organizations are trying to define a general or
domain-specific framework of digital twin. [Natis-Gartner2017]
proposed that building a digital twin of a physical entity requires
four key elements: model, data, monitoring, and uniqueness.
[Tao2019] proposed a five-dimensional framework of digital twin {PE,
VE, SS, DD, CN}, in which PE represents physical entity, VE
represents virtual entity, SS represents service, DD represents twin
data, and CN represents the connection between various components.
[ISO-2021] issued a draft standard for digital twin manufacturing
system, and proposed a reference framework including data collection
domain, device control domain, digital twin domain, and user domain.
3.2. Digital Twin for Networks
Communication networks provide a solid foundation for implementing
various 'digital twin' applications. At the same time, in the face
of increasing business types, scale and complexity, a network itself
also needs to use digital twin technology to seek enhanced and
optimized solutions compared to relying solely on the real network.
The motivation for network digital twin can somehow be traced back to
some earlier concepts, such as "shadow MIB", inductive modeling
techniques, parallel systems, etc. Since 2017, the application of
digital twin technology in the field of communication networks has
gradually been researched as illustrated by the (non-exhaustive) list
of examples that are listed hereafter.
Within academia, [Dong2019] established the digital twin of 5G mobile
edge computing (MEC) network, used the twin offline to train the
resource allocation optimization and normalized energy-saving
algorithm based on reinforcement learning, and then updated the
scheme to MEC network. [Dai2020] established a digital twin edge
network for mobile edge computing system, in which a twin edge server
is used to evaluate the state of entity server, and the twin mobile
edge computing system provides data for training offloading strategy.
[Nguyen2021] discusses how to deploy a digital twin for complex 5G
networks. [Hong2021] presents a digital twin platform towards
automatic and intelligent management for data center networks, and
then proposes a simplified the workflows of network service
management. [Dai2022] gives the concept of digital twin and proposes
an digital twin-enabled vehicular edge computing (VEC) network, where
digital twin can enable adaptive network management via the two-
closed loops between physical VEC networks and digital twins. In
addition, international workshops dedicated to digital twin in
networking field have already appeared, such as IEEE DTPI 2021&2022-
Digital Twin Network Online Session [DTPI2021], [DTPI2022], and IEEE
NOMS 2022 - TNT workshop [TNT2022].
Although the application of digital twin technology in networking has
started, the research of digital twin for networks technology is
still in its infancy. Current applications focus on specific
scenarios (such as network optimization), where network digital twin
is just used as a network simulation tool to solve the problem of
network operation and maintenance. Combined with the characteristics
of digital twin technology and its application in other industries,
this document believes that network digital twin can be regarded as
an indispensable part of the overall network system and provides a
general architecture involving the whole life cycle of real network
in the future, serving the application of network innovative
technologies such as network planning, construction, maintenance and
optimization, improving the automation and intelligence level of the
network.
4. Characteristics of Network Digital Twin
So far, there is no standard definition for characteristic of
"network digital twin" within the networking industry. This document
introduces four key elements (i.e., data, models, mapping, and
interfaces) to characterize the network digital twin. These four
elements can be integrated into a network management system to
analyze, diagnose, emulate, and control the real network. To that
aim, a real-time and interactive mapping is required between the real
network and its virtual twin network. Whether a Digital Twin
supports all or a subset of the functions above (i.e., analyze,
diagnose, emulate, and control) is deployment specific.
Referring to the characteristics of digital twin in other industries
and the characteristics of the networking itself, the digital twin
network should involve at least four key elements: data, mapping,
models and interfaces as shown in Figure 1.
+-------------+ +--------------+
| | | |
| Mapping | | Interface |
| | | |
+-------------+-----------------+--------------+
| |
| Analyze, Diagnose |
| |
| +----------------------+ |
| | Network Digital Twin | |
| +----------------------+ |
+------------+ +------------+
| | Emulate, Control | |
| Models | | Data |
| |------------------------| |
+------------+ +------------+
Figure 1: Key Elements of Network Digital Twin
Data: A network digital twin should maintain historical data and/or
real time data (configuration data, operational state data,
topology data, trace data, metric data, process data, etc.) about
its real-world twin (i.e. real network) that are required by the
models to represent and understand the states and behaviors of the
real-world twin.
The data is characterized as the single source of "truth" and
populated in the data repository, which provides timely and
accurate data service support for building various models.
Models: Techniques that involve collecting data from one or more
sources in the real-world twin and developing a comprehensive
representation of the data (e.g., system, entity, or process)
using specific models. These models are used as emulation and
diagnosis basis to provide dynamics and elements on how the live
real network operates and generates reasoning data utilized for
decision-making.
Various models such as service models, data models, dataset
models, or knowledge graph can be used to represent the real
network assets and, then, instantiated to serve various network
applications.
Interfaces: Standardized interfaces ensure the interoperability of
network digital twin. There are two major types of interfaces:
* The interface between the network digital twin platform and the
real network infrastructure.
* The interface between network digital twin platform and
applications.
: The former provides real-time data collection and control on the
real network. The latter helps in delivering application requests to
the network digital twin platform and exposing the various platform
capabilities to applications.
Mapping: Used to identify the digital twin and the underlying
entities and establish a real-time interactive relation between
the real network and the twin network or between two twin
networks. The mapping can be:
* One to one (pairing, vertical): Synchronize between a real
network and its virtual twin network with continuous flows.
* One to many (coupling, horizontal): Synchronize among virtual
twin networks with occasional data exchange.
Such mappings provide a good visibility of actual status, making
the digital twin suitable to analyze and understand what is going
on in the real network. It also allows using the digital twin to
optimize the performance and maintenance of the real network.
The network digital twin constructed based on the four core
technology elements can analyze, diagnose, emulate, and control the
real network in its whole life cycle with the help of optimization
algorithms, management methods, and expert knowledge. One of the
objectives of such control is to master the network digital twin
environment and its elements to derive the required system behavior,
e.g., provide:
* repeatability: that is the capacity to replicate network
conditions on-demand.
* reproducibility: i.e., the ability to replay successions of
events, possibly under controlled variations.
and "the mirroring pace and scope" should be controlled for a given
twin instance.
Note: Realtime interaction is not always mandatory for all twins.
For example, when assessing some configuration changes or
emulating some innovative techniques, the digital twins can behave
as an isolated simulation platform without the need of realtime
telemetry data. It might be useful to have interactive mapping
capability so that the validated changes can be evaluated under
real network conditions whenever required by the testers. Whether
realtime interaction between virtual and real network is mandatory
is a configurable parameter. Adequate validation guards have to
be enforced at both twin and physical network. Enabling realtime
interaction in network digital twin is a catalyst to achieve
autonomous networks or self-driven network.
5. Benefits of Network Digital Twin
Network digital twin can help enabling closed-loop network management
across the entire lifecycle, from deployment and emulation, to
visualized assessment, physical deployment, and continuous
verification. By doing so, network operators and end-users to some
extent, as allowed by specific application interfaces, can maintain a
global, systemic, and consistent view of the network. Also, network
operators and/or enterprise user can safely exercise the enforcement
of network planning policies, deployment procedures, etc., without
jeopardizing the daily operation of the real network.
The main difference between network digital twin and simulation
platform is the use of interactive virtual-real mapping to build
closed-loop network automation. Simulation platforms are the
predecessor of the network digital twin, one example of such a
simulation platform is network simulator [NS-3], which can be seen as
a variant of network digital twin but with low fidelity and lacking
for interactive interfaces to the real network. Compared with those
classical approaches, key benefits of network digital twin can be
summarized as follows:
(a) Using real-time data to establish high fidelity twins, the
effectiveness of network simulation is higher; then the
simulation cost will be relatively low.
(b) The impact and risk on running networks is low when
automatically applying configuration/policy changes after the
full analysis and required verifications (e.g., service impact
analysis) within the twin network.
(c) The faults of the real network can be automatically captured by
analyzing real-time data, then the correction strategy can be
distributed to the real network elements after conducting
adequate analysis within the twins to complete the closed-loop
automatic fault repair.
The following subsections further elaborate such benefits in details.
5.1. Optimized Network Total Cost of Operation
Large scale networks are complex to operate. Since there is no
effective platform for simulation, network optimization designs have
to be tested on the real network at the cost of jeopardizing its
daily operation and possibly degrading the quality of the services
supported by the network. Such assessment greatly increases network
operator's Operational Expenditure (OPEX) budgets too.
With a network digital twin platform, network operators can safely
emulate candidate optimization solutions before deploying them on the
real network. In addition, operator's OPEX on the real network
deployment will be greatly decreased accordingly at the cost of the
complexity of the assessment and the resources involved.
5.2. Optimized Decision Making
Traditional network operation and management mainly focus on
deploying and managing running services, but hardly support
predictive maintenance techniques.
Network digital twin can combine data acquisition, big data
processing, and AI modeling to assess the status of the network, but
also to predict future trends, and better organize predictive
maintenance. The ability to reproduce network behaviors under
various conditions facilitates the corresponding assessment of the
various evolution options as often as required.
5.3. Safer Assessment of Innovative Network Capabilities
Testing a new feature in an operational network is not only complex,
but also extremely risky. Service impact analysis is required to be
adequately achieved prior to effective activation of a new feature.
Network digital twin can greatly help assessing innovative network
capabilities without jeopardizing the daily operation of the real
network. In addition, it helps researchers to explore network
innovation (e.g., new network protocols, network AI/ML applications)
efficiently, and network operators to deploy new technologies quickly
with lower risks. Take AI/ ML application as example, it is a
conflict between the continuous high reliability requirement (i.e.,
99.999%) and the slow learning speed or phase-in learning steps of
AI/ML algorithms. With network digital twin, AI/ML can complete the
learning and training with the sufficient data before deploying the
model in the real network. This would encourage more network AI
innovations in future networks.
5.4. Privacy and Regulatory Compliance
The requirements on data confidentiality and privacy on network
providers increase the complexity of network management, as decisions
made by computation logics such as an SDN controller may rely upon
the packet payloads. As a result, the improvement of data-driven
management requires complementary techniques that can provide a
strict control based upon security mechanisms to guarantee data
privacy protection and regulatory compliance. This may range from
flow identification (using the archetypal five-tuple of addresses,
ports and protocol) to techniques requiring some degree of payload
inspection, all of them considered suitable to be associated to an
individual person, and hence requiring strong protection and/or data
anonymization mechanisms.
With strong modeling capability provided by the network digital twin,
very limited real data (if at all) will be needed to achieve similar
or even higher level of data-driven intelligent analysis. This way,
a lower demand of sensitive data will permit to satisfy privacy
requirements and simplify the use of privacy-preserving techniques
for data-driven operation.
5.5. Customized Network Operation Training
Network architectures can be complex, and their operation requires
expert personnel. Network digital twin offers an opportunity to
train staff for customized networks and specific user needs. Two
salient examples are the application of new network architectures and
protocols or the use of "cyber-ranges" to train security experts in
threat detection and mitigation.
6. Challenges to Build Network Digital Twin
According to [Hu2021], the main challenges in building and
maintaining digital twins can be summarized as the following five
aspects:
* Data acquisition and processing
* High-fidelity modeling
* Real-time, two-way communication between the virtual and the real
twins
* Unified development platform and tools
* Environmental coupling technologies
Compared with other industrial fields, digital twin in networking
field has its unique characteristics. On one hand, network elements
and system have higher level of digitalization, which implies that
data acquisition and virtual-real communication are relatively easy
to achieve. On the other hand, there are various different type of
network elements and typologies in the network field; and the network
size is characterized by the numbers of nodes and links in it but the
network size growth pace can not meet the service needs, especially
in the deployment of end to end service which spans across multiple
administrative domains. So, the construction of a digital twin
network system needs to consider the following major challenges:
Large scale challenge: A digital twin of large-scale networks will
significantly increase the complexity of data acquisition and
storage, the design and implementation of relevant models. The
requirements of software and hardware of the network digital twin
system will be even more constraining. Therefore, efficient and
low cost tools in various fields should be required. Take data as
an example, massive network data can help achieve more accurate
models. However, the cost of virtual-real communication and data
storage becomes extremely expensive, especially in the multi-
domain data-driven network management case, therefore efficient
tools on data collection and data compression methods must be
used.
Interoperability: Due to the inconsistency of technical
implementations and the heterogeneity of vendor adopted
technologies, it is difficult to establish a unified digital twin
network system with a common technology in a network domain.
Therefore, it is needed firstly to propose a unified architecture
of network digital twin, in which all components and
functionalities are clear to all stakeholders; then define
standardized and unified interfaces to connect all network twins
via ensuring necessary compatibility.
Data modeling difficulties: Based on large-scale network data, data
modeling should not only focus on ensuring the accuracy of model
functions, but also has to consider the flexibility and
scalability to compose and extend as required to support large
scale and multi-purpose applications. Balancing these
requirements further increases the complexity of building
efficient and hierarchical functional data models. As an optional
solution, straightforwardly clone the real network using
virtualized resources is feasible to build the twin network when
the network scale is relatively small. However, it will be of
unaffordable resource cost for larger scales network. In this
case, network modeling using mathematical abstraction or
leveraging the AI algorithms will be more suitable solutions.
Real-time requirements: Network services normally have real-time
requirements, the processing of model simulation and verification
through a network digital twin will introduce the service latency.
Meanwhile, the real-time requirements will further impose
performance requirements on the system software and hardware.
However, given the nature of distributed systems and propagation
delays, it is challenge to keep network digital twins in sync or
auto-sync between real network and network digital twin.
Changes to the digital object automatically drive changes in the
real object can be even challenging. To address these
requirements, the function and process of the data model need to
be based on automated processing mechanism under various network
application scenarios. On the one hand, it is needed to design a
simplified process to reduce the time cost for tasks in network
twin as much as possible; on the other hand, it is recommended to
define the real-time requirements of different applications, and
then match the corresponding computing resources and suitable
solutions as needed to complete the task processing in the twin.
Security risks: A network digital twin has to synchronize all or
subset of the data related to involved real networks in real time,
which inevitably augments the attack surface, with a higher risk
of information leakage, in particular. On one hand, it is
mandatory to design more secure data mechanism leveraging legacy
data protection methods, as well as innovative technologies such
as block chain. On the other hand, the system design can limit
the data (especially raw data) requirement on building digital
twin network, leveraging innovative modeling technologies such as
federal learning.
To address the above listed challenges, it is important to agree on a
unified architecture of network digital twin, which defines the main
functional components and interfaces (Section 7). Then, relying upon
such an architecture, it is required to continue researching on the
key enabling technologies including data acquisition, data storage,
data modeling, interface standardization, and security assurance.
7. A Reference Architecture of Network Digital Twin
Based on the definition of the key network digital twin technology
elements introduced in Section 4, a network digital twin architecture
is depicted in Figure 2. This network digital twin architecture is
broken down into three layers: Application Layer, Digital Twin Layer,
and Real Network Layer.
+---------------------------------------------------------+
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
|Capability Exposure| Intent Input
| |
+-------------+-------------------v-----------------------+
| Instance of Network Digital Twin |
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Repo- | | +-----+-----^------+ | | Twin | |
| | sitory | | | | | | Network| |
| | | | +-----v-----+------+ | | Mgmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^----------------------------+-------------------+
| |
| data collection | control
+--------+----------------------------v-------------------+
| Real Network |
| |
+---------------------------------------------------------+
Figure 2: Reference Architecture of Network Digital Twin
Real Network: All or subset of network elements in the real network
exchange network data and control messages with a network digital
twin instance, through twin-real control interfaces. The real
network can be a mobile access network, a transport network, a
mobile core, a backbone, etc. The real network can also be a data
center network, a campus enterprise network, an industrial
Internet of Things, etc.
The real network can span across a single network administrative
domain or multiple network administrative domains. And, the real
network can include both physical entities and some virtual
entities (e.g. vSwitches, NFVs, etc.), which together carry
traffic and provide actual network services.
This document focuses on the IETF related real network such as IP
bearer network and data center network.
Digital Twin Layer: This layer includes three key subsystems: Data
Repository subsystem, Service Mapping Models subsystem, and
Network Digital Twin Management subsystem. These key subsystems
can be placed in one single network administrative domain and
provide the service to the application (e.g.,SDN controller) in
other network administrative domain, or lied in every network
administrative domain and coordinate between each other to provide
services to the application in the upper layer.
One or multiple network digital twin instances can be built and
maintained:
* Data Repository subsystem is responsible for collecting and
storing various network data for building various models by
collecting and updating the real-time operational data of
various network elements through the twin southbound interface,
and providing data services (e.g., fast retrieval, concurrent
conflict handling, batch service) and unified interfaces to
Service Mapping Models subsystem.
* Service Mapping Models complete data modeling, provide data
model instances for various network applications, and maximizes
the agility and programmability of network services. The data
models include two major types: basic and functional models.
- Basic models refer to the network element model(s) and
network topology model(s) of the network digital twin based
on the basic configuration, environment information,
operational state, link topology and other information of
the network element(s), to complete the real-time accurate
characterization of the real network.
- Functional models refer to various data models used for
network analysis, emulation, diagnosis, prediction,
assurance, etc. The functional models can be constructed
and expanded by multiple dimensions: by network type, there
can be models serving for a single or multiple network
domains; by function type, it can be divided into state
monitoring, traffic analysis, security exercise, fault
diagnosis, quality assurance and other models; by network
lifecycle management, it can be divided into planning,
construction, maintenance, optimization and operation.
Functional models can also be divided into general models
and special-purpose models. Specifically, multiple
dimensions can be combined to create a data model for more
specific application scenarios. New applications might need
new functional models that do not exist yet. If a new model
is needed, ‘Service Mapping Models’ subsystem will be
triggered to help creating new models based on data
retrieved from ‘Data Repository’.
* Network Digital Twin Management fulfils the management function of
network digital twin, records the life-cycle transactions of the
twin entity, monitors the performance and resource consumption of
the twin entity or even of individual models, visualizes and
controls various elements of the network digital twin, including
topology management, model management and security management.
Notes: 'Data collection' and 'change control' are regarded as
network-facing interfaces between virtual and real network. From
implementation perspective, they may form a sub-layer or sub-
system to provide common data collection and change control
functions, enabled by a specific infrastructure supporting bi-
directional flows and facilitating data aggregation, action
translation, pre-processing, and ontologies. It might not be
possible or necessary to 'synchronize' all twin state or flows
from twin entity to physical entity or network management system.
Bi-directional interaction means that: data, state, or flows are
reported or collected from the physical network or the network
management system to a twin instance, and configure changes or
'necessary' data sent from a twin instance to physical.
Application Layer: Various applications (e.g., Operations,
Administration, and Maintenance (OAM)) can effectively run over a
network digital twin platform to implement either conventional or
innovative network operations, with low cost and less service impact
on real networks. Network applications make requests that need to be
addressed by the network digital twin. Such requests are exchanged
through a northbound interface, so they are applied by service
emulation at the appropriate twin instance(s).
8. Enabling Technologies to Build Network Digital Twin
This section briefly describes several key enabling technologies to
build digital twin work system, based on the challenges and the
reference architecture described in above sections. Actually, each
enabling technology is worth of deep researching respectively and
separately.
8.1. Data Collection and Data Services
Data collection technology is the foundation of building data
repository for network digital twin. Target driven mode should be
adopted for data collection from heterogeneous data sources. The
type, frequency and method of data collection shall meet the
application of network digital twin. Whenever building network
models for a specific network application, the required data can be
efficiently obtained from the data repository.
Diverse existing tools and methods (e.g., SNMP, NETCONF [RFC6241],
IPFIX [RFC7011], and telemetry [RFC9232]) can be used to collect
different type of network data. YANG data models and associated
mechanisms defined in [RFC8639][RFC8641] enable subscriber-specific
subscriptions to a publisher's event streams. Such mechanisms can be
used by subscriber applications to request for a continuous and
customized stream of updates from a YANG datastore. Moreover, some
innovative methods (e.g., sketch-based measurement) can be used to
acquire more complex network data, such as network performance data.
Furthermore, data transformation and aggregation capabilities can be
used to improve the applicability on network modelling. Toward
building data repository for a digital twin system, data collection
tools and methods should be as lightweight as possible, so as to
reduce the volume of required network equipment resources, and
meaningful so it can be useful. Several solutions related to data
collection are work-in-progress in IETF/IRTF, e.g., adaptive
subscription [I-D.ietf-netconf-adaptive-subscription], efficient data
collection [I-D.zcz-nmrg-digitaltwin-data-collection], and contextual
information [I-D.claise-opsawg-collected-data-manifest].
Data repository works to effectively store large-scale and
heterogeneous network data, as well provide data and services to
build various network models. So, it is also necessary to study
technologies regarding data services including fast search, batch-
data handling, conflict avoidance, data access interfaces, etc.
8.2. Network Modeling
The basic network element models and topology models help generate
virtual twin of the network according to the network element
configuration, operation data, network topology relationship, link
state and other network information. Then the operation status can
be monitored and displayed, and the network configuration change and
optimization strategy can be pre-verified.
For small scale network, network simulating tools (e.g., [NS-3],
[Mininet], etc.) and emulating tools (e.g., [EVE-NG], [GNS-3]) can be
used to build basic network models. By using the packet processing
capability of virtual network element, such tools can quickly verify
the functions of the control plane and data plane. However, this
modeling method also has many limitations, including high resource
consumption, poor performance analysis ability, and poor scalability.
For large scale network, mathematical abstraction methods can be used
to build basic network models efficiently. Knowledge graph, network
calculus, and formal verification can be candidate methods. Some
relevant researches have emerged in recent years, such as [Hong2021],
[G2-SIGCOMM], and [DNA-2022]. Going forward, how to improve the
extensibility and accuracy of the models is still a big challenge.
As an example, the theory of bottleneck structures introduced in
[G2-SIGCOMM], [G2-SIGMETRICS] can be used to construct a mathematical
model of the network (see also
[I-D.giraltyellamraju-alto-bsg-requirements] for more info). A
bottleneck structure is a computational graph that efficiently
captures the topology, the routing and flow properties of the
network. The graph embeds the latent relationships that exist
between bottlenecks and the application flows in a distributed
system, providing an efficient mathematical framework to compute the
ripple effects of perturbations (e.g., a flow arriving or departing
from the system, or the dynamic change in capacity of a wireless
link, among others). Because these perturbations can be seen as
mathematical derivatives of the communication system, bottleneck
structures can be used to compute optimized network configurations,
providing a natural engineering sandbox for building network models.
One of the key advantages of bottleneck structures is that they can
be used to compute (symbolically or numerically) key performance
indicators of the network (e.g., expected flow throughput, projected
flow completion time, etc.) without the need to use computationally
intensive simulators. This capability can be especially useful when
building a digital twin or a large-scale network, potentially saving
orders or magnitude in computational resources in comparison to
simulation or emulation-based approaches.
The functional model aims to realize the dynamic evolution of network
performance evaluation and intelligent decision-making. Data driven
AI/ML algorithm will play a great role in building complex network
functional models. As a research hotspot in recent years, many
successfully cases have been demonstrated, such as [RouteNet],
[MimicNet], etc. In the future, in addition to improving the
generalization ability and interpretability of AI models, we also
need to focus on how to improve the real-time and interactivity of
model reasoning based on data and control in network digital twin
layer.
8.3. Network Visualization
It is the internal requirement of the network digital twin system to
use network visibility technology to visually present the data and
model in the network twin with high fidelity and intuitively reflect
the interactive mapping between the real network entity and the
network twin. Network Visibility technology can help users
understand the internal structure of the network, and also help mine
valuable information hidden in the network.
Network Visibility can use algorithms such as hierarchical layout,
heuristic layout or force oriented layout (or a combination of
several algorithms) for topology layout. The related topology data
can be acquired using solutions provided in [RFC8345], [RFC8346],
[RFC8944], etc. Meanwhile, network digital twin system can select
different interaction methods or combinations of interaction methods
to realize the visual dynamic interaction mapping of virtual and real
networks. The data query technology, such as SPARQL, can be used to
express queries across diverse data sources, whether the data is
stored natively as RDF or viewed as RDF via middleware.
8.4. Interfaces
Based on the reference architecture, there are three types of
interfaces on building a network digital twin system:
(d) Network-facing interfaces are twin interfaces between the real
network and its twin entity. They are responsible for
information exchange between real network and network digital
twin. The candidate interfaces can be SNMP, NETCONF, etc.
(e) Application-facing interfaces are Application-facing interfaces
between the network digital twin and applications. They are
responsible for information exchange between network digital
twin and network applications. The lightweight and extensible
[RESTFul] interface can be the candidate northbound interface.
(f) Internal interfaces are within network digital twin layer. They
are responsible for information exchange between the three
subsystems: Data Repository, Service Mapping Models, and Digital
Twin Network Management. These interfaces should be of high-
speed, high-efficiency and high-concurrency. The candidate
interfaces or protocols can be XMPP [RFC7622] or HTTP/3.0
[RFC9114].
All these interfaces are recommended to be open and standardized
interfaces so as to avoid either hardware or software vendor lock,
and achieve interoperability. Besides the interfaces list above,
some new interfaces or protocols can be created to better serve
digital twin network system.
8.5. Twinning Management
Twinning management is the key to the efficient deployment and
potential value of network digital twin systems in production
networks. Twinning management technology inputs all information and
data from each step of network business into the constructed model
through the construction of digital threads for optimization,
prediction, and guidance. Then, the implementation results are
analyzed to see if they meet expectations, and any actions are fed
back to form a closed loop. Twinning management involves various
network components (e.g., controller, orchestrator) and domains
(security, for example) from end to end, including, but not limited
to, the following main technologies:
* Orchestration of twins: Manage and organize multiple twin model
instances, including the creation, deletion, storage, version
control, and deployment of model instances, and arrange required
modeling resources as needed to maximize resource utilization
efficiency.
* Collaboration Management: Coordinate multiple participants, such
as network administrators, data scientists, security teams, etc.,
to ensure the accuracy and real-time performance of the twins.
Involve collaborative tools, workflow design, data sharing, and
permission control to promote cooperation and information sharing
among all parties.
* Conflict Detection and Resolution: Identify and address conflicts
including user intents, access control policies, or multiple
applications interacting within the digtial twin netowrk system.
Conflict detection and resolution techniques may use various
mechanisms, such as rule-based policies, role-based access
control, or dynamic conflict resolution algorithms (e.g.
[Pradeep2022] and [Zheng2022]).
* Energy-Efficient Twinning: Focus on energy efficiency in digital
twin network system. It includes monitoring and optimizing the
energy consumption of both network equipment and digital twin
system operation, reducing the energy expenditure of network
operation, and achieving the goal of green network.
9. Interaction with Intent-Based Networking (IBN)
Intent-based, means that users can input their abstract 'intent' to
the network, instead of detailed policies or configurations on the
network devices. [RFC9315] clarifies the concept of "Intent" and
provides an overview of IBN functionalities. The key characteristic
of an IBN system is that user intent can be assured automatically via
continuously adjusting the policies and validating the real-time
situation.
IBN can be envisaged in a network digital twin context to show how
network digital twin improves the efficiency of deploying network
innovation. To lower the impact on real networks, several rounds of
adjustment and validation can be emulated on the network digital twin
platform instead of directly on real network. Therefore, the digital
twin network can be an important enabler platform to implement IBN
systems and fooster their deployment.
10. Sample Application Scenarios
Network digital twin can be applied to solve different problems in
network management and operation.
10.1. Human Training
The usual approach to network OAM with procedures applied by humans
is open to errors in all these procedures, with impact in network
availability and resilience. Response procedures and actions for
most relevant operational requests and incidents are commonly defined
to reduce errors to a minimum. The progressive automation of these
procedures, such as predictive control or closed-loop management,
reduce the faults and response time, but still there is the need of a
human-in-the-loop for multiples actions. These processes are not
intuitive and require training to learn how to respond.
The use of network digital twin for this purpose in different network
management activities will improve the operators performance. One
common example is cybersecurity incident handling, where "cyber-
range" exercises are executed periodically to train security
practitioners. Network digital twin will offer realistic
environments, fitted to the real production networks.
10.2. Machine Learning Training
Machine Learning requires data and their context to be available in
order to apply it. A common approach in the network management
environment has been to simulate or import data in a specific
environment (the ML developer lab), where they are used to train the
selected model, while later, when the model is deployed in
production, re-train or adjust to the production environment context.
This demands a specific adaption period.
Network digital twin simplifies the complete ML lifecycle development
by providing a realistic environment, including network topologies,
to generate the data required in a well-aligned context. Dataset
generated belongs to the network digital twin and not to the
production network, allowing information access by third parties,
without impacting data privacy.
10.3. DevOps-Oriented Certification
The potential application of CI/CD models network management
operations increases the risk associated to deployment of non-
validated updates, what conflicts with the goal of the certification
requirements applied by network service providers. A solution for
addressing these certification requirements is to verify the specific
impacts of updates on service assurance and Service Level Agreements
(SLAs) using a network digital twin environment replicating the
network particularities, as a previous step to production release.
Network digital twin control functional block supports such dynamic
mechanisms required by DevOps procedures.
10.4. Network Fuzzing
Network management dependency on programmability increases systems
complexity. The behavior of new protocol stacks, API parameters, and
interactions among complex software components are examples that
imply higher risk to errors or vulnerabilities in software and
configuration.
Network digital twin allows to apply fuzzing testing techniques on a
twin network environment, with interactions and conditions similar to
the production network, permitting to identify and solve
vulnerabilities, bugs and zero-days attacks before production
delivery.
10.5. Network Inventory Management
With the development of enterprise digitization, the number of
enterprise Internet of Objects (IoT) devices, virtualized Cloud
software inventory component (e.g., virtual firewall), and network
hardware inventory (e.g., switches or routers) also increases. The
endpoints connected to an enterprise network lack coherent modelling
and lifecycle management because different services are modelled,
collected, processed, and stored separately. The same category of
network devices (including network endpoints) may be repeatedly
discovered, processed, and stored. Therefore, the inventory is
difficult to manage when they are tracked in different places without
formal synchronization procedures.
Network digital twin management can be used as a means to ensure
consistent representation and reporting of inventory component types.
In doing so, the enforcement of security policies and assessment will
be further simplified. Such an approach will ease implementing a
unified control strategy for all inventory components types connected
to an enterprise network. It also make actors on assets more
accountable for breaching their compliance promises. Special care
should be considered to protect the inventory data since it may be
gather privacy-sensitive information.
The network inventory management for twins or various inventory
components can be used, for example, to exercise the implication of
End of Life (EoL), dependency, and hardware dependency "what-if"
scenarios.
11. Research Perspectives: A Summary
Research on network digital twin has just started. This document
presents an overview of the network digital twin concepts and
reference architecture. Looking forward, further elaboration on
network digital twin scenarios, requirements, architecture, and key
enabling technologies should be investigated by the industry, so as
to accelerate the implementation and deployment of digital twin
network.
12. Security Considerations
This document describes concepts and definitions of digital twin
network. As such, the following security considerations remain high
level, i.e., in the form of principles, guidelines or requirements.
Security considerations of the network digital twin include:
* Secure the digital twin system itself.
* Data privacy protection.
Securing the network digital twin system aims at making the digital
twin system operationally secure by implementing security mechanisms
and applying security best practices. In the context of digital twin
network, such mechanisms and practices may consist in data
verification and model validation, mapping operations between real
network and digital counterpart network by authenticated and
authorized users only.
Synchronizing the data between the real network and the twin network
may increase the risk of sensitive data and information leakage.
Strict control and security mechanisms must be provided and enabled
to prevent data leaks.
13. IANA Considerations
This document has no requests to IANA.
14. Open issues
Refer to: https://github.com/cheneyzhoucheng/network-digital-twin/
issues (https://github.com/cheneyzhoucheng/network-digital-twin/
issues).
15. Informative References
[Dai2020] IEEE Transactions on Industrial Informatics, "Deep
Reinforcement Learning for Stochastic Computation
Offloading in Digital Twin Networks", August 2020.
[Dai2022] Journal of Communications and Information Networks,
"Adaptive Digital Twin for Vehicular Edge Computing and
Networks", March 2022.
[DNA-2022] NSDI 22, "Differential Network Analysis, USENIX Symposium
on Networked Systems Design and Implementation", 2023.
[Dong2019] IEEE Transactions on Wireless Communications, "Deep
Learning for Hybrid 5G Services in Mobile Edge Computing
Systems: Learn from a Digital Twin", July 2019.
[DTPI2021] "IEEE International Conference on Digital Twins and
Parallel Intelligence - Digital Twin Network Session",
July 2021, <https://www.dtpi.org/video/10>.
[DTPI2022] "IEEE International Conference on Digital Twins and
Parallel Intelligence - Digital Twin Network Session",
October 2022, <https://c.trvqd.com/#/television?id=1584825
225713631235&preview=2>.
[EVE-NG] "Emulated Virtual Environment Next Generation", n.d.,
<https://www.eve-ng.net/>.
[Fuller2020]
IEEE Access, "Digital Twin: Enabling Technologies,
Challenges and Open Research", 2020.
[G2-SIGCOMM]
ACM SIGCOMM, "Designing data center networks using
bottleneck structures", August 2021.
[G2-SIGMETRICS]
ACM SIGMETRICS, "On the Bottleneck Structure of
Congestion-Controlled Networks", December 2019.
[GNS-3] "Graphical Network Simulator-3, GNS3", n.d.,
<https://www.gns3.com/>.
[Grieves2014]
"Digital twin: Manufacturing excellence through virtual
factory replication", 2003,
<https://www.3ds.com/fileadmin/PRODUCTS-
SERVICES/DELMIA/PDF/Whitepaper/DELMIA-APRISO-Digital-Twin-
Whitepaper.pdf>.
[Hong2021] ACM SIGCOMM 2021 Workshop on Network-Application
Integration (NAI' 21), "NetGraph: An Intelligent Operated
Digital Twin Platform for Data Center Networks", 2021.
[Hu2021] Journal of Intelligent Manufacturing and Special
Equipment, "Digital twin: a state-of-the-art review of its
enabling technologies, applications and challenges", 2021.
[I-D.claise-opsawg-collected-data-manifest]
Claise, B., Quilbeuf, J., Lopez, D., Martinez-Casanueva,
I. D., and T. Graf, "A Data Manifest for Contextualized
Telemetry Data", Work in Progress, Internet-Draft, draft-
claise-opsawg-collected-data-manifest-06, 10 March 2023,
<https://datatracker.ietf.org/doc/html/draft-claise-
opsawg-collected-data-manifest-06>.
[I-D.giraltyellamraju-alto-bsg-requirements]
Ros-Giralt, J., Yellamraju, S., Wu, Q., Contreras, L. M.,
Yang, Y. R., and K. Gao, "Supporting Bottleneck Structure
Graphs in ALTO: Use Cases and Requirements", Work in
Progress, Internet-Draft, draft-giraltyellamraju-alto-bsg-
requirements-03, 23 September 2022,
<https://datatracker.ietf.org/doc/html/draft-
giraltyellamraju-alto-bsg-requirements-03>.
[I-D.ietf-netconf-adaptive-subscription]
Wu, Q., Song, W., Liu, P., Ma, Q., Wang, W., and Z. Niu,
"Adaptive Subscription to YANG Notification", Work in
Progress, Internet-Draft, draft-ietf-netconf-adaptive-
subscription-04, 12 December 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-netconf-
adaptive-subscription-04>.
[I-D.zcz-nmrg-digitaltwin-data-collection]
Zhou, C., Chen, D., Martinez-Julia, P., and Q. Ma, "Data
Collection Requirements and Technologies for Digital Twin
Network", Work in Progress, Internet-Draft, draft-zcz-
nmrg-digitaltwin-data-collection-03, 9 July 2023,
<https://datatracker.ietf.org/doc/html/draft-zcz-nmrg-
digitaltwin-data-collection-03>.
[ISO-2021] ISO, "Digital Twin manufacturing framework - Part 2:
Reference architecture: ISO/CD 23247-2", 2021,
<https://www.iso.org/standard/78743.html>.
[Madni2019]
"Leveraging digital twin technology in model-based systems
engineering", January 2019.
[MimicNet] ACM SIGCOMM 2021 Conference (SIGCOMM ’21), "MimicNet: Fast
Performance Estimates for Data Center Networks with
Machine Learning", 2021.
[Mininet] "Mninet: An Instant Virtual Network on your Laptop", n.d.,
<http://mininet.org/>.
[Natis-Gartner2017]
"Innovation insight for digital twins - driving better
IoT-fueled decisions", 2017,
<https://www.gartner.com/en/documents/3645341>.
[Nguyen2021]
IEEE Communications Magazine, "Digital Twin for 5G and
Beyond", February 2021.
[NS-3] "Network Simulator, NS-3", n.d., <https://www.nsnam.org/>.
[Pradeep2022]
"Conflict Detection and Resolution in IoT Systems: A
Survey. IoT 2022", February 2022.
[RESTFul] O'Reilly Media, Inc, "RESTful Web APIs", 2013.
[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/rfc/rfc6241>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/rfc/rfc7011>.
[RFC7622] Saint-Andre, P., "Extensible Messaging and Presence
Protocol (XMPP): Address Format", RFC 7622,
DOI 10.17487/RFC7622, September 2015,
<https://www.rfc-editor.org/rfc/rfc7622>.
[RFC8345] Clemm, A., Medved, J., Varga, R., Bahadur, N.,
Ananthakrishnan, H., and X. Liu, "A YANG Data Model for
Network Topologies", RFC 8345, DOI 10.17487/RFC8345, March
2018, <https://www.rfc-editor.org/rfc/rfc8345>.
[RFC8346] Clemm, A., Medved, J., Varga, R., Liu, X.,
Ananthakrishnan, H., and N. Bahadur, "A YANG Data Model
for Layer 3 Topologies", RFC 8346, DOI 10.17487/RFC8346,
March 2018, <https://www.rfc-editor.org/rfc/rfc8346>.
[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
<https://www.rfc-editor.org/rfc/rfc8639>.
[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, <https://www.rfc-editor.org/rfc/rfc8641>.
[RFC8944] Dong, J., Wei, X., Wu, Q., Boucadair, M., and A. Liu, "A
YANG Data Model for Layer 2 Network Topologies", RFC 8944,
DOI 10.17487/RFC8944, November 2020,
<https://www.rfc-editor.org/rfc/rfc8944>.
[RFC9114] Bishop, M., Ed., "HTTP/3", RFC 9114, DOI 10.17487/RFC9114,
June 2022, <https://www.rfc-editor.org/rfc/rfc9114>.
[RFC9232] Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
A. Wang, "Network Telemetry Framework", RFC 9232,
DOI 10.17487/RFC9232, May 2022,
<https://www.rfc-editor.org/rfc/rfc9232>.
[RFC9315] Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", RFC 9315, DOI 10.17487/RFC9315, October
2022, <https://www.rfc-editor.org/rfc/rfc9315>.
[Rosen2015]
IFAC-Papersonline, "About the importance of autonomy and
DTs for the future of manufacturing", 2015.
[RouteNet] IEEE Journal on Selected Areas in Communication (JSAC),
"RouteNet:Leveraging Graph Neural Networks for network
modeling and optimization in SDN", October 2020.
[Tao2019] IEEE Transactions on Industrial Informatics, "Digital Twin
in Industry: State-of-the-Art", April 2019.
[TNT2022] "IEEE International workshop on Technologies for Network
Twins", 2022, <https://noms2022.ieee-noms.org/ws4-1st-
international-workshop-technologies-network-twins-tnt-
2022>.
[Zheng2022]
"Intent Based Networking management with conflict
detection and policy resolution in an enterprise network,
Computer Networks, Volume 219", December 2022.
Acknowledgments
Many thanks to the NMRG participants for their comments and reviews.
Thanks to Daniel King, Quifang Ma, Laurent Ciavaglia, Jérôme
François, Jordi Paillissé, Luis Miguel Contreras Murillo, Alexander
Clemm, Qiao Xiang, Ramin Sadre, Pedro Martinez-Julia, Wei Wang,
Zongpeng Du, Peng Liu, Christopher Janz, and Albrecht Schwarz.
Diego Lopez and Antonio Pastor were partly supported by the European
Commission under Horizon 2020 grant agreement no. 833685 (SPIDER),
and grant agreement no. 871808 (INSPIRE-5Gplus).
Authors' Addresses
Cheng Zhou
China Mobile
Beijing
China
Email: zhouchengyjy@chinamobile.com
Hongwei Yang
China Mobile
Beijing
China
Email: yanghongwei@chinamobile.com
Xiaodong Duan
China Mobile
Beijing
China
Email: duanxiaodong@chinamobile.com
Diego Lopez
Telefonica I+D
Seville
Spain
Email: diego.r.lopez@telefonica.com
Antonio Pastor
Telefonica I+D
Madrid
Spain
Email: antonio.pastorperales@telefonica.com
Qin Wu
Huawei
Nanjing
210012
China
Email: bill.wu@huawei.com
Mohamed Boucadair
Orange
Rennes
France
Email: mohamed.boucadair@orange.com
Christian Jacquenet
Orange
Rennes
France
Email: christian.jacquenet@orange.com
